What are the responsibilities and job description for the Staff Scientist - Reservations position at Uber?
About The Role
The Reservations data science team owns the experience and algorithms powering the Uber Reserve Product in the Rider and Driver app. We optimize the user experience for both Riders and Drivers, matching, dispatch and pricing algorithms, as well as growth levers to deliver magical experiences and business value.
The Reserve product is moving from a nascent mvp product to a more mature product, we have delivered on most of the low hanging fruit" initiatives to improve the experience, building levers to customize the product and handle edge cases to consistently deliver high reliability. Currently, our focus is shifting towards driving efficiency to improve our unit economics and to drive adoption and growth.
Some of the key projects involve major changes in Pricing, Matching and Dispatch that involve transitioning from heuristic based decision making to building machine learning models to make key decisions in real time, as well as important changes to Driver and Rider experience, including discoverability and awareness generation levers, and finally piloting new use cases that leverage the Reserve Tech as a platform to unlock new vectors of growth.
What The Candidate Will Need / Bonus Points
What the Candidate Will Do
The Reservations data science team owns the experience and algorithms powering the Uber Reserve Product in the Rider and Driver app. We optimize the user experience for both Riders and Drivers, matching, dispatch and pricing algorithms, as well as growth levers to deliver magical experiences and business value.
The Reserve product is moving from a nascent mvp product to a more mature product, we have delivered on most of the low hanging fruit" initiatives to improve the experience, building levers to customize the product and handle edge cases to consistently deliver high reliability. Currently, our focus is shifting towards driving efficiency to improve our unit economics and to drive adoption and growth.
Some of the key projects involve major changes in Pricing, Matching and Dispatch that involve transitioning from heuristic based decision making to building machine learning models to make key decisions in real time, as well as important changes to Driver and Rider experience, including discoverability and awareness generation levers, and finally piloting new use cases that leverage the Reserve Tech as a platform to unlock new vectors of growth.
What The Candidate Will Need / Bonus Points
What the Candidate Will Do
- Deploy a wide variety of methodologies, including causal inference techniques, funnel analyses, and econometric modeling to identify our largest business opportunities.
- Work together with Product, Operations, and Engineering partners to design a roadmap of features and initiatives as well as the long-term team strategy.
- Run large scale experiments to validate the impact of new features.
- Present findings to business and executive audiences
- Ph.D., M.S. or Bachelor's degree in Statistics, Economics, Mathemathics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- 6 years of industry experience as an Applied or Data Scientist or equivalent.
- Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn), underpinned by a solid grasp of MLOps practices, including design documentation, testing, and source code management with Git.
- Agile project management capabilities, adept at using tools like JIRA, and a driven problem-solver with a passion for impacting the retail sector at scale.
- Advanced skills in the development and deployment of large-scale ML models
- Experience in experimental design and analysis (e.g., A/B and market-level experiments), causal inference.
- Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
- Strong experience in causal inference, optimization, and machine learning.
- Experience in algorithm development and prototyping. Particularly in Pricing domain.
- Ability to drive clarity on the best modeling or analytic solution for a business objective
- Expertise in causal inference, A/B testing designs, multivariate testing, and other advanced analytical methods.
- Experience in designing highly scalable, resilient systems for customer-facing applications and familiarity with optimization techniques.
- Design, develop, productionize, and operate econometric models, experiments, and frameworks to assess challenging causal problems such as product incrementality and long-term value
- Propose, design, and analyze large scale online experiments
- Build statistical, optimization, and machine learning models for a range of applications.
- Design and execute product experiments and interpret the results to draw detailed and actionable conclusions.
- Knowledge of advertising targeting and measurement solutions, digital marketing analytics tools, and specific technologies such as GCP, BQ, and Elastic/SOLR/Vector Search.
- Collaborate with cross-functional teams across disciplines such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productionization
Salary : $216,000 - $240,000